Postdoctoral Fellow (Carnegie Mellon University)
5000 Forbes Avenue
Gates Building 8125
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA, 15213
Postdoctoral Fellow, Carnegie Mellon University, 2013-present (w/ Christos Faloutsos)
Postdoctoral Reseacher, University of Massachusetts Amherst, 2011-2013 (w/ Don Towsley)
Ph.D. University of Massachusetts Amherst, 2010 (w/ Don Towsley)
Co-organizing the 7th NetSciCom Workshop 2015 in Hong Kong (@ IEEE INFOCOM 2015). More details to be announced soon! Deadline should be around mid December 2014.
Co-organized NetSci'14 Symposium Temporal Networks, Human Dynamics and Social Physics. Thanks to all the authors and invited speakers that made the symposium such a success (a really full auditorium)!
- Bruno Ribeiro, Christos Faloutsos,
Modeling Website Popularity Competition in the Attention-Activity Marketplace, WSDM 2015 (accepted) [arXiv:1403.0600].
How does a new startup drive the popularity of competing websites into oblivion like Facebook famously did to MySpace? This question is of great interest to academics, technologists, and financial investors alike. In this work we exploit the singular way in which Facebook wiped out the popularity of MySpace, Hi5, Friendster, and Multiply to guide the design of a new popularity competition model. Our model provides new insights into what Nobel Laureate Herbert A. Simon called the "marketplace of attention," which we recast as the attention-activity marketplace. Our model design is further substantiated by user-level activity of 250,000 MySpace users obtained between 2004 and 2009. The resulting model not only accurately fits the observed Daily Active Users (DAU) of Facebook and its competitors but also predicts their fate four years into the future.
- Pinghui Wang, John C.S. Lui, Bruno Ribeiro, Don Towsley, Junzhou Zhao, Xiaohong Guan,
Efficiently Estimating Motif Statistics of Large Networks, ACM Transactions on Knowledge Discovery from Data (TKDD), Sept. 2014 [pdf].
Older ArXiv version arXiv:1306.5288.
Exploring statistics of locally connected subgraph patterns (also known as network motifs) has helped researchers better understand the structure and function of biological and online social networks (OSNs). Nowadays the massive size of some critical networks – often stored in already overloaded relational databases – effectively limits the rate at which nodes and edges can be explored, making it a challenge to accurately discover subgraph statistics. In this work, we propose sampling methods to accurately estimate subgraph statistics from as few queried nodes as possible. We present sampling algorithms that efficiently and accurately estimate subgraph properties of massive networks. Our algorithms require no pre-computation or complete network topology information. At the same time, we provide theoretical guarantees of convergence. We perform experiments using widely known data sets, and show that for the same accuracy, our algorithms require an order of magnitude less queries (samples) than the current state-of-the-art algorithms.
- Ting-Kai Huang, Bruno Ribeiro, Harsha V. Madhyastha, Michalis Faloutsos,
The Socio-monetary Incentives of Online Social Network Malware Campaigns, ACM Conference on Online Social Networks (COSN'14), 2014 [pdf] [slides.pdf] [slides.pptx].
Online social networks (OSNs) offer a rich medium of malware propagation. Unlike other forms of malware, OSN malware campaigns direct users to malicious websites that hijack their accounts, posting malicious messages on their behalf with the intent of luring their friends to the malicious website, thus triggering word-of-mouth infections that cascade through the network compromising thousands of accounts. * But how are OSN users lured to click on the malicious links? *
In this work, we monitor 3.5 million Facebook accounts and explore the role of pure monetary, social, and combined socio-monetary psychological incentives in OSN malware campaigns. Among other findings we see that the majority of the malware campaigns rely on pure social incentives. However, we also observe that malware campaigns using socio-monetary incentives infect more accounts and last longer than campaigns with pure monetary or social incentives. The latter suggests the efficiency of an epidemic tactic surprisingly similar to the mechanism used by biological pathogens to cope with diverse gene pools.
- Flavio Figueiredo, Jussara M Almeida, Yasuko Matsubara, Bruno Ribeiro, Christos Faloutsos,
Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries, (ECML/PKDD 2014) [arXiv:1405.1459].
How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate and model the effect of revisits on popularity. A revisit can be defined as a repeated play (song or video) or a re-tweet of the same hashtag over time. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of time series.
- Bruno Ribeiro,
Modeling and Predicting the Growth and Death of Membership-based Websites, WWW 2014 [pdf] [arXiv:1307.1354] [slides.pptx].
Carnegie Mellon University Press Release: In Age of Information Overload, Ability To Sustain Attention Determines Success | CMU SCS Press Release
How the Model Works commentary (last updated Feb 6 2014)
In the news: CNET | The Times of India | Pittsburgh Post Gazette | ACM TechNews | Phys.org news | Futurity.org | The Connectivist | The Tartan | Business Standard | Pittsburgh Business Times | Business Insider (I never said that "website audiences that grew via word-of-mouth are much better off in the long-run for retaining and engaging users", in fact my work has a number of counter-examples to this claim) | e! Science News | Network World | Science Daily News | TechAdvisor | MIT Technology Review: A roundup of the most interesting innovations coming out of research centers and universities
| NSF's Facebook Page
Driven by outstanding success stories of Internet startups such as Facebook and The Huffington Post, recent studies have thoroughly described their growth. These highly visible online success stories, however, overshadow an untold number of similar ventures that fail. The study of website popularity is ultimately incomplete without general mechanisms that can describe both successes and failures. In this work we present six years of the daily number of users (DAU) of twenty-two membership-based websites -- encompassing online social networks, grassroots movements, online forums, and membership-only Internet stores -- well balanced between successes and failures. We then propose a combination of reaction-diffusion-decay processes whose resulting equations seem not only to describe well the observed DAU time series but also provide means to roughly predict their evolution. This model allows an approximate automatic DAU-based classification of websites into self-sustainable v.s. unsustainable and whether the startup growth is mostly driven by marketing & media campaigns or word-of-mouth adoptions.
- (alphabetical) K. Avrachenkov, P. Basu, G. Neglia, B. Ribeiro*, and D. Towsley,
Pay Few, Influence Most: Online Myopic Network Covering, IEEE NetSciCom Workshop 2014 (Best Paper Award) [pdf] [slides.pptx].
Older version as Technical Report arXiv.
Efficient marketing or awareness-raising campaigns seek to recruit a small number, $w$, of influential individuals -- where $w$ is the campaign budget -- that are able to cover the largest possible target audience through their social connections.
To date, most of the related literature on maximizing this network cover assumes that the social network topology is known.
Even in such a case the coverage problem is NP-hard. In practice, the network topology is generally unknown and needs to be discovered on-the-fly.
In this paper we assume that the topology is gradually discovered thanks to recruited individuals disclosing their social connections.
Our goal is to provide an efficient online algorithm that recruits individuals so as to maximize the size of target audience covered by the campaign.
We analyze the performance of a variety of online myopic algorithms (i.e. that do not have a priori information on the topology) currently used to sample and search large networks, including the Flickr and YouTube networks.
We also propose a new greedy online algorithm, Maximum Expected Uncovered Degree (MEUD).
Our proposed algorithm greedily maximizes the expected size of the cover, but it requires the degree distribution to be known.
For a class of random power law networks we show that MEUD simplifies into a straightforward procedure, denoted as MOD because it requires only the knowledge of the Maximum Observed Degree.
We substantiate our analytical results with extensive simulations and show that MOD significantly outperforms all analyzed myopic algorithms.
- Bo Jiang, Liyuan Sun, Daniel R. Figueiredo, Bruno Ribeiro, Don Towsley,
On the duration and intensity of cumulative advantage competitions and the struggle of the fittest, [arXiv:1402.4536].
At the heart of competition for resources lie two simple principles: "survival of the fittest", which reflects how intrinsic fitnesses of competing agents influence their ability to gather resources; and "cumulative advantage", which reflects how accumulated resources help gather even more resources. In this letter we show that competitions embodying just the essence of these two principles exhibit a variety of surprising behaviors with respect to their duration, as measured by how long it takes for an indisputable winner to emerge, and intensity, as measured by how many times agents have equal amount of resources. We demonstrate that when the fitnesses of the agents are different, long-lasting competitions continue to occur in spite of a dramatic decrease in their intensity and the certainty that the fitter agent will win. We also unveil an unexpected phenomenon that we call "struggle of the fittest", where long-lasting competitions become more probable when one agent is slightly fitter than when agents are equally fit. These results have important implications for our understanding of competitions and various applications that leverage such models.